Artificial Intelligence Based Three-Phase Unified Power Quality Conditioner

Abstract

Power quality is an important measure of the performance of an electrical power system. This paper discusses the topology, control strategies using artificial intelligent based controllers and the performance of a unified power quality conditioner for power quality improvement. UPQC is an integration of shunt and series compensation to limit the harmonic contamination within 5 %, the limit imposed by IEEE-519 standard. The novelty of this paper lies in the application of neural network control algorithms such as model reference control and Nonlinear Autoregressive-Moving Average (NARMA)–L2 control to generate switching signals for the series compensator of the UPQC system. The entire system has been modeled using MATLAB 7.0 toolbox. Simulation results demonstrate the applicability of MRC and NARMA-L2 controllers for the control of UPQC.